Automatic LocomotionDesignan d Experimen ts for a Modular Robotic System

chestpeeverIA et Robotique

13 nov. 2013 (il y a 8 années et 1 mois)

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Automatic Locomotion Design and Experiments
for a Modular Robotic System
Akiya Kamimura,Haruhisa Kurokawa,Eiichi Yoshida,
,Satoshi Murata,
Kohji Tomita,
,and Shigeru Kokaji,
Abstract—This paper presents a designmethodandexperiments
for whole-bodylocomotionbyamodular robot.There are twotypes
of locomotion for modular robots:a repeating self-reconfiguration
and whole-body motion such as walking or crawling.For whole-
body locomotion,designing a control method is more difficult than
for ordinary robots because a modular robotic system can form
various configurations,each of which has many degrees of free-
dom.This study proposes a unified framework for automatically
designing an efficient locomotion controller suitable for any mod-
ule configuration.The method utilizes neural oscillators (central
pattern generators,CPGs),each of which works as a distributed
joint controller of eachmodule,anda genetic algorithmto optimize
the CPGnetwork.We verified the method by software simulations
and hardware experiments,in which our modular robotic system,
named M-TRAN II,performed stable and effective locomotion in
various configurations.
Index Terms—Central pattern generator (CPG),evolutionary
computation,locomotion,modular robot,neural oscillator net-
NRECENT years,hardware and software experiments have
addressed the feasibility of reconfigurable robotic systems
[1]–[18].Existing reconfigurable modular robots comprise ho-
mogeneous or heterogeneous robotic modules,whose configu-
ration and locomotion patterns can change according to given
tasks or the environment.Self-reconfigurable systems are able
to self-adapt to the external environment by changing con-
figurations,as well as self-repair through replacement of dis-
abled parts with spare modules.The unique capabilities of self-
reconfigurable modular robots are thought to be applicable in
extreme or unknown environments,such as on distant plan-
ets,in deep seas,inside nuclear plants,and in disaster areas
for exploration or search-and-rescue operations where human
access is difficult.Our goal is to realize such a versatile self-
reconfigurable modular robotic systemby studying both module
hardware designs and control algorithms for adaptive structure
formation and behavior.
Manuscript received August 25,2003;revised December 14,2004.This
study was supported by the Science and Technology Research Grant Program
for Young Researchers with a Term from the Ministry of Education,Culture,
Sports,Science,andTechnology(MEXT) of Japan.RecommendedbyTechnical
Editor M.Meng.
A.Kamimura,H.Kurokawa,E.Yoshida,K.Tomita,and S.Kokaji are
with the National Institute of Advanced Industrial Science and Technology
(AIST),Ibaraki 305-8564,Japan (;kurokawa-;;;
S.Murata is with the Tokyo Institute of Technology,Yokohama 226-8502,
Japan (
Digital Object Identifier 10.1109/TMECH.2005.848299
The main research topics on the self-reconfigurable modu-
lar robotic systems have been module design,development of
module hardware,distributed algorithms for two- and three-
dimensional structural formation [9],[10],[14]–[18],and loco-
motion by modules.
There are two types of modular robot locomotion.One type
is a repeating configuration change,e.g.,individually sending
a module from the tail of the module structure to the head [7],
[16]–[18].The other is whole-body locomotion,such as walking
and crawling,which is achieved by controlling joint motors
coordinately without any configuration change [7],[11],[13].
Inthis paper,we deal withwhole-bodylocomotionanddevelopa
unified framework applicable to any given module configuration
for generating a locomotion pattern and controller.
The following aspects should be considered for whole-body
locomotion by modular robots.
1) Design of a locomotion controller (centralized or decen-
2) A method for generating locomotion structures and pat-
An asynchronous decentralized controller is desirable to re-
duce calculation cost on each module.
Fewstudies have dealt with whole-body locomotion for mod-
ular robots.Yim demonstrated caterpillar-like locomotion and
a rolling track in which each module motion is based on the
specific gait control table corresponding to each module config-
uration [13].Shen,Salemi,and Will proposed a distributed con-
trol method to accomplish locomotion and self-reconfiguration
using a unified framework,which is based on hormonelike mes-
sage propagation and a rule base for a particular global be-
havior [11].We demonstrated various locomotive motions and
reconfigurations using our M-TRAN I module.We used the
same control method used by Yim,in which several locomotive
motions and reconfigurations were achieved mechanically [7].
Although these studies provide a good means to control a mul-
tidegree of freedom system like modular robots,they do not
describe how to make gait control tables or rule bases suitable
for various module configurations.
Sims [19] and Lipson and Pollack [20] proposed gen-
eration methods for both robotic structures and locomotion
patterns (locomotion controllers) with an evolutionary compu-
tation method.They succeeded in generating various simple
robotic structures that can move in unique locomotion patterns.
In the biomimetic research field,several studies have ad-
dressed generating biped or quadruped locomotion by using
neural oscillators [21]–[23],where phase differences between
joint angles are determined by interactions among connected
1083-4435/$20.00 ©2005 IEEE
Fig.1.Schematic view of an M-TRAN module.
nonlinear oscillators called central pattern generators (CPGs).
There are several studies that automatically find a locomotion
pattern by optimizing CPGparameters by using the evolutionary
computation method [24],[25].These methods are promising
because they are asynchronous,decentralized,and suitable for
modular robots.
The possible modular robot locomotion structures and pat-
terns are infinite because this robot has many joints,and the
scale and shape of the module structure are changeable.De-
signing a stable or optimal locomotion pattern only for a fixed-
module configuration is difficult,in general because there are
many degrees of freedom.Therefore,in this study we focus on
generating whole-body locomotion by modular robots and de-
velop a unified framework for generating practical locomotion
patterns for any given module configuration.To realize this,we
applied the CPGmodel ( [21]–[23]) as a distributed locomotion
controller and generalized the model to be applicable to a scal-
able module structure.We also developed software combining a
dynamics simulator with an evolutionary computation method
to evaluate module locomotion performance and to maximize
efficiency of the locomotion controller.
We completed hardware experiments to demonstrate the fea-
sibility of the proposed method.The obtained locomotion con-
troller based on the neural oscillator model was implemented,
and stable module locomotion was achieved in a distributed
The contents of this paper are as follows.The Section II
presents an overview of our M-TRAN module.In Section III,
a distributed locomotion controller based on the CPG model
and details of automatic locomotion pattern generation (ALPG)
software are clarified,and simulation results for locomotion de-
sign are provided.Section IV describes the hardware design of
M-TRANII and describes hardware experiments on various lo-
comotion patterns.The obtained results are discussed in Section
V,and conclusions are provided in Section VI.
We study whole-body locomotion for modular robots us-
ing our self-reconfigurable modular robot M-TRAN (Modular
Transformer) (shown in Fig.1).This module comprises three
components:two semicylindrical parts and a link part.Each
semicylindrical part can rotate from−90

to 90

using a geared motor embedded in the link.Each semicylindri-
cal part has three connecting surfaces with permanent magnets.
The modules can connect with each other by magnetic force be-
cause the polarity of the magnets between the two parts differs.
Fig.2.Example of possible configurations,a three-dimensional lattice struc-
ture above and a robotic configuration below.Each semicylindrical part of the
module is shown in a different color,and checkerboard-like structures can be
Each connecting surface can be connected to another connect-
ing surface in every orthogonal relation;thereby,various lattice
structures are formed easily,as illustrated in Fig.2.The lattice
structure can be reconfigured by changing positions of the semi-
cylindrical parts,through repetition of simple procedures such
as detaching the connection,rotating the semicylindrical part,
and reconnecting.
This section presents the method that finds a practical loco-
motion pattern for a given module configuration.An efficient
locomotion pattern,realized by a proper locomotion controller,
is automatically generated by genetic algorithm(GA).The GA
optimizes the locomotion controller by evaluating the perfor-
mance of locomotion by dynamic simulations.
A.Overall Structure for Generating the Locomotion Pattern
We constructedautomatic locomotionpatterngenerationsoft-
ware (ALPG hereafter),combining Vortex (CM Labs Simula-
tions,Inc.) as a three-dimensional dynamic simulation library,a
dynamic model of the M-TRAN II module,a decentralized lo-
comotion controller based on a CPG model (Section III-B),
and an optimization method for a CPG network using GA
(Section III-D).The ALPG software outputs an efficient loco-
motion pattern for any given configuration moving in a straight
line in a certain direction.
Fig.3 presents a flowchart of the ALPGsoftware.The mod-
ule configuration and the initial posture are determined first.
Here,“configuration” means a connecting relationship between
modules and “posture” means a set of joint angles for a specific
configuration.In the dynamics simulation,a determined mod-
ule configuration and posture are placed on a flat and horizontal
ground in the virtual world.Performance of the locomotion
pattern is evaluated sequentially by using a fitness function,and
the GA optimizes the CPG network.The obtained results can
be verified by hardware (Sections IV-C and IV-D).
Fig.3.Flow chart of the ALPG software.
B.Decentralized Locomotion Controller Based
on the CPG Model
A locomotion controller for each module drives two joints
according to the outputs of two CPGs as shown in Fig.4.The
interactions among CPGs enable a cooperative motion by mod-
We applied the CPGmodel used in [21]–[23] and generalized
the model to be applicable to a multidegree of freedom system
with an arbitrary number of modules and with any configuration.
The CPG is composed of the same two inhibitively connected
neurons corresponding to extensor and flexor (Fig.4).Each
CPG,described by (1) and (2),is a nonlinear oscillator having
four state variables (u
τ ˙u
= −u
+a · s

= −v
= max(0,u
),i = 0,...,num−1 (1)
τ ˙u
= −u
+a · s

= −v
= max(0,u
),i = 0,...,num−1 (2)
where the subscripts 1 and 2 represent extensor and flexor,and
numrepresents the number of joints,i.e.,CPGs (twice as many
as the number of modules).In those equations,y
is the output of
each of the two neurons,u
is an external input with a constant
value,and w
is a connecting coefficient between extensor and
flexor neuron.The systemwithout the last two terms of the first
equation of both (1) and (2) represents a self-excited oscillator.τ

are time constants of this oscillator,andthe cycle becomes
longer as those variables become larger.Williamson analyzed
that the natural frequency of the oscillator is proportional to 1/τ
if the ratio between τ and τ

is constant [26].It was also reported
that oscillation amplitude is nearly proportional to the external
input u
[21].By adding the two feedback terms,the oscillation
of the system is entrained and the system works cooperatively
with others.
The variables s
and s
in the first equations of (1) and (2)
represent the CPG interaction calculated by (3) and (4).
= 2.0 ·
1 +exp
= 2.0 ·
1 +exp
where weight
is a connection weight between the ith and jth
and feed
are weighted sums of the state variables
) of the ith CPG.The same connection weight is applied
to both u
and u
.The variable s is normalized from −1.0 to
1.0 by the sigmoid function after feed is divided by num,the
number of joints,to be applicable to any size of system.
Alocomotion controller drives two joint motors according to
outputs of two CPGs.The control signals to motors are calcu-
lated by (5) and defined as a voltage input to general DCmotors.
In the following simulation,the dynamics model of the motor
implemented in the Vortex simulator was used.
= −m
The motion of each joint is directly fed back to (1) and (2) as
an angle deviation f between a current angle and the nominal
angle calculated by (6).
= k(angle
= −f
where k is a feedback coefficient,angle
represents a current
angle of ith joint,and the nominal
is a nominal one,
which is given by an initial angle of the joint.The feedback
term f provides the oscillation around the nominal angle.The
amplitude of oscillation and the cycle become smaller when k
The CPG dynamics calculation and the dynamics simulation
of the physical systembythe Vortexsimulator are achievedinde-
pendently in the calculation step (15 ms).In each step,the CPG
dynamics is calculated by using the Runge–Kutta method,and
the results are used for driving the joints in the virtual world.The
resultant module locomotion is evaluated by the GA process.
C.Basic CPG Behavior
Without connection,each CPG oscillates independently.
When connected,all the CPGs oscillate together and converge
to a specific pattern (limit cycle) determined by the network
connection.Such behavior is widely known as a locking phe-
nomenonor entrainment amongconnectednonlinear oscillators.
Fig.4.Schematics illustrations of the locomotion controller (left) and details of the CPG (right).The locomotion controller controls rotation of two joints
according to outputs of two CPGs.
Fig.5.Basic CPGbehavior anda locomotionexample bytwomodules.Graphs
show CPG output versus time.
Moreover,each CPGphysically interacts with the modules’ dy-
namics through the motor and such an interaction affects the
limit cycle.This is called global entrainment.
In our model,only three types of connection between CPGs
) are used so that the GAcan efficiently search a CPG
network for locomotion:1,excitatory connection;−1,inhibitive
connection;or 0,no connection.
In the following,basic CPG behavior is illustrated with the
simple examples shown in Fig.5.
1) Case I:Two CPGs With Connection 1:As shown in the
top graph in Fig.5,two CPGs synchronize together (in-phase).
In this case,the phase difference between CPGs always con-
verges to 0 starting fromany initial CPG state.
2) Case II:Two CPGs With Connection −1:In this case
shown in the middle of Fig.5,the phase difference between two
CPGs always converges to π (antiphase).
3) Case III:Three CPGs in a Loop:When several CPGs
are connected in a loop by connection −1,phase differences
between neighboring CPGs converge to 2π/n (n,number of
CPGs in the loop).In the bottomof Fig.5,phase differences of
the left three CPGs converge to 2π/3 because of this loop.This
CPGnetwork makes the two-module structure move forward or
back by a caterpillar-like motion according to the initial state of
the CPGs.That is,two attractors exist in this case (only one is
shown in the figure).
These three results also demonstrate the effect of global en-
trainment.While the eigen frequency of the stand-alone CPGis
0.55 Hz,the three results are 0.88,0.95,and 1.25 Hz,for the con-
ditions shown in Table I.The frequency and amplitude of joint
motion change according to the network connection,inertia of
the mechanical structure,and effects of external disturbances.
As described above,CPGs can produce various phase differ-
ences autonomously in accordance with the CPG network and
physical motion of modules.
D.Evolutionary Computation
We implemented a GA in the ALPG software to find auto-
matically the optimal CPG network for locomotion.The con-
nection weights (weight
) and the initial values of the CPGs,
(0)] are coevolved by using the GA.
As discussed in Section III-C,the connection weights determine
the phase differences among joints and the shape and stability of
the limit cycle.The initial values have no relation to the locomo-
tion pattern.However,they are important parameters for smooth
convergence to a limit cycle when starting fromthe initial shape
to the locomotive motion.
The constants used in the simulation are summarized in
Table I,with important constants boldfaced.Each value is de-
termined by trial and error,considering mechanical properties
such as maximum motor torque and speed,and weight and in-
ertia.It is possible to implement these values as variables in
the GA process,but using them will expand the search space.
Although it is also possible to use different values of k or mfor
each module,they are identical here to take into consideration
module exchangeability and scalability.
By the following GAprocesses,locomotion patterns for mov-
ing straight with little energy consumption are obtained by op-
timizing both connection weights and initial values of CPGs.
1) Parameter Optimization:Connection weights among
CPGs (weight
) are selected fromthree values,−1 (inhibitive
connection),0 (no connection),and 1 (excitatory connection).
In the CPG model used in this study,the sign of the connection
weight is essential todetermine phase differences.Therefore,we
express the connection weights in discrete values even though
connection weights can be expressed in real numbers.Initial
values of CPGs (u(0),v(0)) are real numbers from−8.0 to 8.0
and from0.0 to 3.0.These ranges were determined empirically
by repeating simulation.
2) Fitness Evaluation:In the first stage of the GA,connec-
tion weights and initial values of CPGs are randomly initialized
by the population size,pop
size (see Table I).Virtual locomo-
tion after a fixed interval (15 s) is evaluated individually by
using the fitness function represented by (7).
fitness = a · length −b · width −
c · loss
where a,b,and c are weighting coefficients and are fixed to 200,
250,and 0.47,considering the balance among the following
three parameters:1) length is the moving distance of the center
of gravity,2) width is the maximumdeviation fromthe straight
line,3) loss is the energy loss,which is an accumulated value
of the total consumed energy by all the motors (we used a time
integration of the product of the torque and the angular velocity
of each joint) during the evaluation interval.The GA searches
for locomotionparameters withhigher fitness values.The fitness
function above leads the module structure to move faster along
the straight line with less energy consumption.
3) Selection,Crossover,and Mutation Procedures:Each in-
dividual is sorted by the fitness value after the evaluation proce-
dure for all individuals in one generation.The lower groups are
deleted according to the selection rate,s
rate (see Table I).
A crossover procedure is achieved to fill the deleted parts
by selecting parents from the remaining individuals by using a
roulette selection method.The crossover procedures for connec-
tion weights and initial values are achieved separately because
the former are discrete whereas the latter are continuous.The
Fig.6.Examples of tested configurations.(a) Four-legged configuration.(b)
H-shaped structure whose configuration is the same as (a).(c) Six-legged con-
figuration.(d) Wheel configuration.(e) Thread configuration.(f) Another type
of four-legged configuration.
Fig.7.Obtained locomotion patterns.(a) Walking pattern.(b) Wave-like pat-
tern.For configurations in Fig.6(a) and (b).Arrows in the figure show the
moving direction.
N-point crossover method is used for the connection weights
),while the unimodal normal distribution crossover
(UNDX) method [27] is used for the initial values.The lat-
ter method is known to be superior for optimizing multimodal
functions,i.e.,functions with many local minima.
After the crossover procedures,several individuals are se-
lected randomly according to the mutation rate,m
rate (see Ta-
ble I).In the mutation procedure,the initial values [u(0),v(0)]
of the selected individual are varied in a narrowrange and a part
of the connection weight matrix is initialized randomly to −1,
0,or 1.
The evaluation procedure then restarts with the new gener-
ation.The GA process stops when the number of generations
exceeds a maximumnumber of generations,max
gene (see Ta-
ble I),or the fitness average becomes constant.Aquasioptimized
locomotion pattern will emerge after the GA processes above
are repeated.
4) Application to Various Module Configurations:A loco-
motion controller suitable for locomotion in any module config-
urations can be produced by the ALPG.To show the feasibility
of this method,we applied it to various module configurations
(Fig.6).Stable locomotion was realized for all configurations.
The obtained locomotion patterns by two structures in Fig.6(a)
and (b) are shown in Fig.7.These two patterns differ completely
despite their identical configurations (connecting relationship
Fig.8.Fitness versus generation curves for configurations shown in Fig.6.
between modules).The former is a walking pattern and the lat-
ter is a wavelike motion.This result shows that the initial posture
affects the locomotion pattern:walking is efficient for legged
configurations and a wavelike motion is better for a flattened
configuration.This comes fromfeedback terms,f
,in (1) and
Fig.8 plots the fitness versus generation curve of each con-
figuration fromFig.6(a)–(f).Each fitness value is calculated in
the same metrics,and it is confirmed that the fitness value of
the wheel shape [Fig.6(d)] is the best among the six module
structures,i.e.,most effective for moving rapidly with lower en-
ergy on flat ground.Almost the same result (fitness value) was
obtained for the four-legged configuration after several trials.
Fig.9(a) illustrates motion in the phase space (the angle and
angular velocity) of the front leg base joint in Fig.6(a).Fig.9(b)
shows the cyclic change of every motor angle in Fig.6(a).Every
motor angle oscillates with a constant frequency (1.15 Hz) and
a fixed-phase difference by entrainment among CPGs and the
mechanical structure.For stable locomotive motions,both en-
trainment between CPGs and entrainment between mechanical
structures and CPGs (global entrainment) are important.In other
words,when the pendulumswing of the mechanical structure is
not matched to the rhythm made by CPGs,locomotive motion
becomes neither periodic nor stable.In our model,the rhythm
made by the mechanical structure is applied to each CPGmodel
as a change of the rotation angle of each joint represented by
All oscillation cycles of the obtained locomotion patterns are
listed in Table II.The oscillation of the six-legged configuration,
Fig.6(c),is the slowest among six samples.This is due to the
weight of the whole body being supported on only several of the
six short legs;the cycle of the legs becomes long.As a result,
all the oscillations are entrained and the cycle is long.
The software simulation above confirmed that the ALPG
method can generate various locomotive motions according
to the given module configurations.The required time for the
ALPGto create a locomotion pattern depends on the number of
Fig.9.(a) Phase space trajectory (angle versus angular velocity) of the base
joint of the front leg in Fig.6(a).(b) All joint angle trajectories for the motion
in Fig.7(a).The oscillation cycle is 1.15 Hz.
modules and the number of contacts in the virtual world between
modules and the ground at each step.It took about 6 h to obtain
the stable walking pattern for the nine-module configuration in
Fig.6(a) with a 2.53 GHz Pentium4 processor PC.
5) Analysis of the CPGNetwork and Stability:We analyzed
the acquired CPG network for the four-legged configuration
[Fig.6(a)] and locomotion stability by the CPG network.
Fig.10 illustrates the obtained connection network on CPG
numbers 6 and 8,which are placed on the base joints of the front
legs of the four-legged configuration.In that figure,nine mod-
ules and 18 CPGs are shown.The solid lines showan excitatory
connection and the dotted lines showan inhibitive connection.A
nearly symmetrical connection network is obtained by the evo-
lutional computation.Fig.11 shows the feedback signals,S
and S
,input to CPGnumbers 6 and 8.The antiphase feedback
signals are generated by a CPG interaction determined by the
connection network,enabling the four-legged configuration to
move straight with a trotting gait.
To confirm the stability of the locomotion made by the CPG
network,we carried out a simulation in which an external force
is added twice on the front right leg of the four-legged configura-
tion while walking.Fig.12 shows that the disturbed rhythmwas
soon recovered.This demonstrates that locomotion by CPGs is
robust to external disturbances.
Fig.10.CPG network connections of two CPGs (numbers 6 and 8) of the four-legged configuration in Fig.6(a).
Fig.11.Feedback signals S
and S
for the motion in Fig.7(a).
Fig.12.Trajectories of the joint angle (in thick line) and the CPGoutput when
external force is added on the front leg.The rhythmof the joint rotation is soon
recovered after the external force is removed.
This section describes hardware experiments of modular
robot locomotion.The developed module hardware named M-
TRAN II and its control system are explained first.Two types
of locomotion experiments are then presented.
The first locomotion experiments were carried out by down-
loading the obtained locomotion sequences (time series data)
for all joints to the modules and playing back locomotion ac-
cording to the sequence.The objective of the experiments is to
confirmthe validity of dynamic simulation by ALPG software.
The second locomotion experiments were carried out to
achieve adaptive locomotion on varying terrain conditions by
applying real-time CPG control.We compared results by play-
back control.
Fig.13.M-TRAN II module.
Fig.14.Inner structure of the M-TRAN II module.
A.Development of M-TRAN II Module Hardware
We showed robotic motion and transformation performance
using our modular robotic system,M-TRAN I [7].The newly
developed system (M-TRAN II),shown in Figs.13 and 14,
is much improved compared with M-TRAN I,especially in
CPU processing speed,the communication system,the motor
controller,maximum torque and speed,the power supply us-
ing a battery,power consumption,and size and weight.These
improvements enabled real-time CPG control and stand-alone
Fig.13 shows two semicylindrical parts,called a passive part
and an active part.The passive part has four permanent magnets
on each of three surfaces (S pole outside).Global communica-
tion electrodes are placed symmetrically on the same surface.As
shown in Fig.14,the passive part contains a CPUcircuit,power
Fig.15.Control and communication systemused in M-TRAN II.
supply,and a battery.The CPUcircuit includes a microprocessor
(SH7047,Renesas Technology Corp.) called a Main-CPU.We
applied a CANBUS(1 Mbps) systemfor intermodule communi-
cation.An infrared LEDand a sensor are installed on each CPU
circuit board and each passive connection surface as a proximity
sensor.Each module can operate independently using its CPU
and the battery.Currently,battery life is about 30 min.
The active part holds three disconnection mechanisms us-
ing permanent magnets,nonlinear springs,and shape memory
alloy (SMA) coils based on an internally balanced magnetic
unit (IBMU) [28].A microprocessor (PIC16F873) called PIC-
Ais also installed for controlling connection/disconnection and
checking connection status.The detailed explanations for the
mechanismare provided in [29].
Twogearedmotors andtheir control circuit boardare installed
inside the link.The control circuit board includes a micropro-
cessor (PIC16F877) called PIC-L,which either realizes a PID
position control or directly drives the motor.
Table III summarizes M-TRAN II module specifications.
More details regarding the mechanical and electrical design of
the M-TRAN II module are available in [30].
B.Control and Communication System in M-TRAN II
Fig.15 shows the communication system used in the M-
TRANII system.Each module is connected to a global commu-
nication bus by way of a Main-CPU with CANBUS (1 Mbps).
The modules can utilize a virtual shared memory realized by
this intermodule communication.The data written in the mem-
ory will be implicitly transferred to all the connected modules
and each module can refer to the status of other modules only
by reading its own memory.
Inside the module,twoPICs (PIC-AandPIC-L) are connected
to the Main-CPU by a local bus,each of which is controlled by
the Main-CPU.The communication speed of the local bus is 38
400 bps.
Two of the 20 developed modules include an RF receiver
(RF Solutions,Inc.) inside,and it is possible to send commands
by remote control to all the connected modules.The received
commands,such as switching directions,changing module con-
figurations and locomotion modes are executed by all modules
at the same time.
C.Hardware Implementation of the Controllers
We performed two types of locomotion experiments:one
using a playback controller and the other using a real-time CPG
In the playback control mode,each module selects a sequence
by each location in an assembled module structure and plays
back the sequence by positioning motors every 15 ms according
to the sequence.All the modules start the playback simultane-
ously by the command from the remote controller.As a result,
whole-body module locomotion can be achieved.There is no
communication between modules in the playback control mode,
i.e.,each module is just repeating the playback of its own se-
In real-time CPGcontrol mode,locomotive motions by mod-
ules are achieved by calculating CPG dynamics,expressed by
(1)–(6) and driving the joints every 15 ms,similar to the ALPG
software.In the real hardware,dynamics for every CPG dy-
namics are calculated in a distributed manner.Each module
calculates its own two CPG dynamics corresponding to its two
joints,and CPG interactions are realized by intermodule com-
munication.The advantage of the proposed system is that cal-
culation cost for locomotion does not change even if the number
of modules increases.Communication cost will increase when
the number of modules increases,but it is not a critical problem
compared with calculation cost,because the current communi-
cation speed is fast enough.
Fig.16.Hardware experiments on various locomotion patterns (a) to (f) in the
playback control mode.A part of the connection in the wheel configuration (d)
was broken after a few rolls.In the experiments above,all modules operate on
an internal battery and no tethers are attached.
D.Locomotion Experiments in Playback Control Mode
Locomotion experiments were carried out by playing back
the sequences with all the configurations (Fig.6).Photos of
the experiments are shown in Fig.16.Movies of the hardware
experiments and simulations,along with other experiments,are
available from our web site [31].All locomotive motions were
achieved by real hardware on flat ground using the same condi-
tions as in the simulation.
Table IV lists moving speeds of module structures in the
hardware experiments and the simulations.These results show
the validity of the dynamic simulation and the implemented
Locomotive motion by real hardware was not achieved for
the wheel configuration [Fig.6(d)].One part of the connection
was broken after several rotations.One reason for this failure is
that the rhythmic joint motion diverged fromthe rolling motion
(the entire rhythm) because there was no feedback from the
physical system in the playback control mode.Excessive force
thus occurred at one connection and exceeded the connection
Fig.17.Adaptation to normal,sticky,and slippery ground by four-legged
robot with real-time CPG control.
E.Locomotion Experiments by Real-Time CPG Controller
We completed locomotion experiments on the four-legged
configuration [Fig.6(a)],the wheel configuration [Fig.6(d)],
and the thread configuration [Fig.6(e)] by using the real-time
CPG controller.
Fig.17 shows locomotion of the four-legged configuration
on normal,sticky,and slippery ground.Fig.18 shows angle
changes of hip joints while walking.In the playback control
mode,hardware locomotion often failed,e.g.,moving direction
was changed drastically or the robot did not move forward,
when experimental conditions such as friction and evenness of
the ground differed fromthose of the simulations.
In the real-time CPG control mode,as shown in Fig.17,the
four-legged robot could adaptively walk on sticky and slippery
ground.This is because walking steps were automatically reg-
ulated according to the conditions of the ground,and phase
differences were always maintained by CPGs (Fig.18).
Fig.19 shows the rolling motion of the wheel configuration
by using the real-time CPG controller.As shown in the figure,
the wheel configuration could roll successfully on flat ground
without breaking the connection.However,on a downslope or
upslope,locomotion became unstable and eventually was out of
control.Results are briefly discussed in Section VD.
Fig.20 shows caterpillar-like locomotion along uneven ter-
rain using the thread configuration.In the playback control
mode,as the robot plays back the same locomotion pattern
on the flat ground as in the simulation,the connection breaks
or there is excessive motor load on uneven ground (Fig.20).
Using the real-time CPG controller,smooth locomotion was
spontaneously realized because the locomotion pattern is au-
tonomously regulated by both real-time CPG interaction and
global entrainment.
This section discusses issues of the CPG connection used in
ALPG software,a comparison between two locomotion con-
trollers,and the necessity of external sensor information and an
upper controller.
A.Structural Symmetry Consideration in GA
Introducing the same mechanical symmetry into the network
structure may reduce the searching space for GA or lead to a
better solution.Consequently,we carried out ALPG simulation
taking into consideration the structural symmetry of the four-
legged configuration shown in Fig.10.In the simulation,CPG
Fig.18.Angle changes of hip joints.The range between the thick lines shows double the amplitude.The amplitude while walking on sticky ground was less than
the amplitudes in other conditions.This means walking steps are automatically changed according to ground conditions.Phase difference is always maintained by
the CPG interaction.
Fig.19.Snapshots of rolling motion by the crawler configuration [Fig.6(d)].
Rolling motion was successfully realized by the real-time CPG control.
Fig.20.Snapshots of caterpillar-like motion by the thread configuration
(Fig.6(e)) in the real-time CPG control mode.It successfully followed along
the uneven part of the ground.
network connections fromhalf of the CPGs (numbers 0,10,11,
2,3,8,9,16,17) were optimized by GA.Connection weights
of the facing CPG were symmetrically applied for the CPG
network connections of the other half of the CPGs.Locomotion
by the symmetrical CPGnetwork was then evaluated in the GA
The resultant fitness value for the obtained locomotion after
several trials was far less than the original results.A possible
reason for this is that structure symmetry cannot be applied
directly to the CPG network.However,a true reason has not
been established,and further investigation is needed.
B.Locally Connected CPG Network
The current CPGcontroller is not applicable to modular robot
systems such as CONRO[11],which have no global communi-
cation line.The CPGconnection should be limited to neighbor-
ing modules for such a system.We tried ALPGsimulation with
this restriction,but could not obtain better locomotion in any
configuration.The main reason for this is that the current CPG
model cannot generate various phase differences without a loop.
In future work,it will be important to propose a CPG model
that can describe phase differences with neighboring CPGs
C.Comparison Between Two Locomotion Controllers
The playback and real-time CPGcontrollers are compared as
follows.Each controller’s advantages correspond to the other
controller’s disadvantages.
The merits of the playback controller are summarized as fol-
1) Easily applied to almost all modular robotic systems be-
cause the controller does not care about a specific type of
2) Position control or simple tracking control by a motor is
3) Intermodule communication is unnecessary as long as the
clocks are synchronized.
The merits of the real-time CPG controller are as follows.
1) Very suited to a distributed system because CPG con-
trollers work in a distributed manner.
2) No explicit synchronization procedure is needed.
3) Possible to cope with rough terrain or unexpected con-
ditions because phase differences among joints are au-
tonomously created and maintained by real-time CPG in-
4) There is no need for storing locomotion sequence data for
all joints in a large volume.
5) Applicable to any configurations simply by changing con-
nection weights among CPGs (connection network).
D.Necessity of External Sensor Information
and Upper Controller
Locomotion by the current CPGmodel is considered reflexive
motion in creatures.External disturbances or changes in circum-
stances can be overcome to some extent by using a rhythmand
phase difference regulation mechanism realized by local feed-
back (angle change,CPG interaction) to a CPG.
However,the mechanismdoes not workproperlywhenrolling
on the downslope by using the wheel configuration (Section
IV-E).In the experiment,the wheel configuration rolled down
with a fixed shape,i.e.,angle changes of each joint were very
small and the rhythmregulation mechanismdid not work.This
shows that the stability range of the controller for this con-
figuration is very narrow.To solve the problem,external sensor
information must be introduced to each CPGto reflect the global
body rhythminto the local rhythmgenerated by each CPG.
Furthermore,an upper level controller is necessary to realize
high-level action,such as detecting obstacles and stepping over
We applied a neural oscillator model (CPG) and its network
to generate stable locomotive motions for modular robotic sys-
tems.We also developed a unified framework by using the CPG
model and the genetic algorithm for creating locomotion suit-
able for any given module structures.
Software simulationbyALPGconfirmedthat locomotive mo-
tions in various configurations can be generated automatically.
We performed two types of locomotion experiments using our
M-TRAN II modules:locomotion by the playback controller
and by the real-time CPG controller.Using the playback con-
troller confirmed that locomotion patterns made by ALPG can
be utilized by real hardware under the same conditions as in the
simulation.Furthermore,we achieved adaptive locomotion on
various terrains using a real-time CPG controller where phase
differences among joints were autonomously regulated by real-
time CPG interactions.
Future work should focus on the following.
1) Construction of a new CPG model integrating external
sensor information.
2) Addition of an upper level controller that enables optional
high-level actions such as detecting an obstacle and step-
ping over it.
3) Development of algorithms for autonomously changing
the whole shape as needed.
The authors thank Dr.S.Ok at the Communications Re-
search Laboratory,Information and Network Systems Division,
Keihanna Human Info-Communications Research Center,Im-
age Group,Japan,for advice regarding the neural oscillator
(CPG) and its implementation.We thank N.Fujii at the De-
partment of Knowledge-based Information Engineering in Toy-
ohashi Universityof Technologyfor aidinganalysis of the neural
oscillator network.
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Akiya Kamimura received the M.E.and Dr.Eng.
degrees from the Graduate School of Engineering,
University of Tokyo,Tokyo,Japan,in 1997 and 2000,
He joined the Mechanical Engineering Labora-
tory,AIST,Massachusetts Institute of Technology,in
2000,and has been conducting research at the Na-
tional Institute of Advanced Industrial Science and
Technology (AIST),Ibasaki,Japan,since 2001.His
research interests include modular robotics and rapid
prototyping systems.
Dr.Kamimura received the Best Paper Award at the 2002 International Sym-
posiumon Distributed Autonomous Robotic Systems (DARS’02).
Haruhisa Kurokawa received the B.E.and
grees in Precision Machinery Engineering and the Dr. in Aero- and Astronautical Engineering
fromthe University of Tokyo,Tokyo,Japan,in 1978,
1981,and 1997,respectively.
He is currently the Head of the Distributed System
Design Research Group,Intelligent Systems Insti-
tute,National Institute of Advanced Industrial Sci-
ence and Technology (AIST),Ibasaki,Japan.His
main research subjects are kinematics of mecha-
nisms,distributed autonomous systems,and nonlin-
ear control.
Eiichi Yoshida (S’94–M’96) received the M.E.and
Dr.Eng.degrees from the Graduate School of Engi-
neering,University of Tokyo,Tokyo,Japan,in 1993
and 1996,respectively.
From1990 to 1991,he worked at the Department
of Microtechnique at Swiss Federal Institute of Tech-
nology,Lausanne (EPFL).He joined the Mechanical
Engineering Laboratory,AIST,MITI in 1996,and
since 2001,he has been conducting research at the
National Institute of Advanced Industrial Science and
Technology (AIST),Ibasaki,Japan.His research in-
terests include decentralized autonomous systems and modular robotics.
Dr.Yoshida received the Best Paper Award at the 1998 International Sym-
posiumon Distributed Autonomous Robotic Systems (DARS’98).
Satoshi Murata (M’01) received the B.E.,M.E.,and
Dr.Eng.degrees in aeronautical engineering from
Nagoya University,Nagoya,Japan,in 1984,1986,
and 1997,respectively.
In 1986,he joined the Mechanical Engineering
Laboratory,AIST,MITI.Since 2001,he has been an
associate professor in Tokyo Institute of Technology.
His current interests include distributed mechanical
systems,modular robotics,and molecular comput-
Dr.Murata received the IEEE-IE Outstanding Pa-
per Award and SICE Outstanding Paper Award in 1991 and 1996,respectively.
He is a member of SICE,RSJ,and JSME.
Kohji Tomita (M’99–A’01) received the B.E.,M.E.,
and PhD degrees from the University of Tsukuba,
Tsukuba,Japan,in 1988,1990,and 1997,respec-
He joined the Mechanical Engineering Labora-
tory,AIST,MITI,in 1990,and has been conducting
research at the National Institute of Advanced Indus-
trial Science and Technology (AIST),Ibasaki,Japan,
as a Senior Research Scientist since 2001.He was
a visiting researcher at Dartmouth College,Hanover,
NH,from2000to 2001.His research interests include
modular robots,distributed software systems,and graph automata.
Shigeru Kokaji (M’00) received the B.E,M.E.,and
Dr.Eng.degrees in precision machinery engineer-
ing from the University of Tokyo,Tokyo,Japan,in
1970,1972,and 1986,respectively.He is currently
the Deputy Director of Intelligent Systems Institute,
National Institute of Advanced Industrial Science and
Technology (AIST),Ibasaki,Japan.His research in-
terests include distributed control of mechanical and
robotic systems.